Several applications such as US published application no. 2002/0102024 to inventors Jones and Viola relate to fast-face detection in digital images and describe certain algorithms. Jones and Viola describe an algorithm that is based on a cascade of increasingly refined rectangular classifiers that are applied to a detection window within an acquired image. Generally, if all classifiers are satisfied, a face is deemed to have been detected, whereas as soon as one classifier fails, the window is determined not to contain a face.
An alternative technique for face detection is described by Froba, B., Ernst, A., “Face detection with the modified census transform”, in Proceedings of 6th IEEE Intl. Conf. on Automatic Face and Gesture Recognition, 17-19 May 2004 Page(s): 91-96. Although this is similar to Violla-Jones each of the classifiers in a cascade generates a cumulative probability and faces are not rejected if they fail a single stage of the classifier. We remark that there are advantages in combining both types of classifier (i.e. Violla-Jones and modified census) within a single cascaded detector.
FIG. 1 illustrates what is described by Jones and Viola. For an analysis of an acquired image 12, the detection window 10 is shifted incrementally by dx pixels across and dy pixels down until the entire image has been searched for faces 14. The rows of dots 16 (not all shown) represent the position of the top-left corner of the detection window 10 at each face detection position. At each of these positions, the classifier chain is applied to detect the presence of a face.
Referring to FIGS. 2a and 2b, as well as investigating the current position, neighboring positions can also be examined, by performing small oscillations around the current detection window and/or varying slightly a scale of the detection window. Such oscillations may vary in degree and in size creating consecutive windows with some degree of overlap between an original window and a second window. The variation may also be in the size of the second window.
A search may be performed in a linear fashion with the dx,dy increments being a pre-determined function of image resolution and detection window size. Thus, the detection is window may be moved across the image with constant increments in x and y directions.
A problem with linear searching occurs when the window size decreases, such as when attempting to detect small faces, and the number of sliding windows that are to be analyzed increases quadratically to the reduction in window size. This results in a compounded slow execution time, making “fast” face detection otherwise unsuitable for real-time embedded implementations.
U.S. application Ser. No. 11/464,083, filed Aug. 11, 2006, which is assigned to the same assignee as the present application, discloses improvements to algorithms such as those described by Jones and Viola, and in particular in generating a precise resolution corresponding to a representation of an image, such as an integral image or a Gaussian image, for subsequent lace detection.